Volume 15 (2024)
Volume 14 (2023)
Volume 13 (2022)
Volume 12 (2021)
Volume 11 (2020)
Volume 10 (2019)
Volume 9 (2018)
Volume 8 (2017)
Volume 7 (2016)
Volume 6 (2015)
Volume 5 (2014)
Volume 4 (2013)
Volume 3 (2012)
Volume 2 (2011)
Volume 1 (2010)
Classification of ASTER Data by Neural Network to Mapping Alterations Related to Copper and Iron Mineralization in Birjand

Jabar Habashi; Majid Mohammady Oskouei; Hadi Jamshid Moghadam

Volume 15, Issue 2 , April 2024, , Pages 649-665


  The studied area located in eastern Iran shows a high potential for various mineralizations, especially copper due to its tectonic activity. Remote sensing data can effectively distinguish these areas because of the sparse vegetation. Therefore, in this study, the ASTER (Advanced Spaceborne Thermal Emission ...  Read More

Determination of Mechanical Parameters of Anthracite Coal using Flying Squirrel Search Algorithm with Timber Load and Displacement Data

Myong Nam Sin; Un Chol Han; Hyon Hyok Ri; Sung Il Jon

Volume 15, Issue 1 , January 2024, , Pages 21-40


  Anthracite coal seam of Democratic People’s Republic of Korea was broken into particles to be soft due to geological tectonic actions through several stages in the Mesozoic era. Because the folds and faults have excessively developed and the shape of coal seam is very complicated, it is impossible ...  Read More

Estimation of geochemical elements using a hybrid neural network-Gustafson-Kessel algorithm

M. Jahangiri; Seyed R. Ghavami Riabi; B. Tokhmechi

Volume 9, Issue 2 , April 2018, , Pages 499-511


  Bearing in mind that lack of data is a common problem in the study of porphyry copper mining exploration, our goal was set to identify the hidden patterns within the data and to extend the information to the data-less areas. To do this, the combination of pattern recognition techniques has been used. ...  Read More

Porosity classification from thin sections using image analysis and neural networks including shallow and deep learning in Jahrum formation

M. Abedini; M. Ziaii; Y. Negahdarzadeh; J. Ghiasi-Freez

Volume 9, Issue 2 , April 2018, , Pages 513-525


  The porosity within a reservoir rock is a basic parameter for the reservoir characterization. The present paper introduces two intelligent models for identification of the porosity types using image analysis. For this aim, firstly, thirteen geometrical parameters of pores of each image were extracted ...  Read More

Rock Mechanics
Prediction of ultimate strength of shale using artificial neural network

S. Moshrefi; K. Shahriar; A. Ramezanzadeh; K. Goshtasbi

Volume 9, Issue 1 , January 2018, , Pages 91-105


  A rock failure criterion is very important for prediction of the ultimate strength in rock mechanics and geotechnics; it is determined for rock mechanics studies in mining, civil, and oil wellborn drilling operations. Also shales are among the most difficult to treat formations. Therefore, in this research ...  Read More

Limestone chemical components estimation using image processing and pattern recognition techniques

F. Khorram; H. Memarian; B. Tokhmechi; H. Soltanian-zadeh

Volume 2, Issue 2 , July 2011, , Pages 126-135


  In this study based on image analysis, an ore grade estimation model was developed. The study was performed at a limestone mine in central Iran. The samples were collected from different parts of the mine and crushed in size from 2.58 cm down to 15 cm. The images of the samples were taken in appropriate ...  Read More

Support vector regression for prediction of gas reservoirs permeability

R. Gholami; A. Moradzadeh

Volume 2, Issue 1 , January 2011


  Reservoir permeability is a critical parameter for characterization of the hydrocarbon reservoirs. In fact, determination of permeability is a crucial task in reserve estimation, production and development. Traditional methods for permeability prediction are well log and core data analysis which are ...  Read More